There have been proposed inventions for searching for content such as television programs and music pieces on the basis of the preferences of users and recommending the content to the users (so-called content personalization) (see, for example, Patent Document 1).
A technique called content-based filtering (CBF) is widely used for the content personalization. In the CBF technique, metadata assigned in advance to content pieces by distributors or sellers is directly used for extraction of preferences or content recommendation. For example, when the content pieces are music pieces, each of the music pieces is assigned in advance metadata such as the title, the artist name, the genre, and the review text. In addition to the information assigned in advance, in some cases, the tempo, rhythm, and the like of the music pieces are detected for additional metadata.
Preference information of a user is generated by using metadata of music pieces as feature vectors and summing the feature vectors of the music pieces according to operations (such as reproduction, recording, skipping, and deletion) performed by the user for the music pieces. For example, the feature vector of a reproduced music piece is multiplied by one, the feature vector of a recorded music piece is multiplied by two, the feature vector of a skipped music piece is multiplied by minus one, and the feature vector of a deleted music piece is multiplied by minus two, before the feature vectors are summed.
When a music piece matching the preferences of the user is to be recommended, a distance (such as cosine correlation) between the feature vector indicating the preferences of the user and the feature vector of each of candidate music pieces is determined, and the music piece for which the determined distance is short is recommended as the music piece matching the preferences of the user.